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所在平台: Udemy |
课程主页: https://www.udemy.com/course/r-programming-language-for-data-scientists-data-science-tm/
课程评论：没有评论
本课程是为数据科学家量身定制的R语言编程课程（数据科学方向）。 **课程亮点:** * **R语言基础与应用:** 掌握R语言的基础知识，并将其应用于数据处理、数据可视化和机器学习。 * **数据科学核心概念:** 深入理解数据收集、预处理、探索性数据分析以及统计建模。 * **端到端数据科学流程:** 全面了解从数据收集到模型部署的整个数据科学工作流程，并通过实际项目加深理解。 * **数据科学家角色与技能:** 认识数据科学家的职责，掌握关键技能、工具和方法论。 * **人工智能与机器学习集成:** 学习如何在数据科学项目中使用人工智能和机器学习技术，并完成端到端的AI/ML项目。 * **R语言进阶:** 学习更高级的R编程技巧，包括数据整理和建模。 **学习成果:** * 熟练掌握R语言编程。 * 能够运用R进行数据处理、可视化和建模。 * 理解数据科学的关键概念和流程。 * 掌握数据科学家所需的核心技能和工具。 * 能够将AI/ML技术应用于实际数据科学问题。
1. R Programming Language for Data Scientists (Data Science)Overview:-This course introduces the R programming language specifically tailored for Data Science applications. It covers the fundamentals of R and its application in data analysis, visualization, and machine learning.Learning Outcomes:-Master the basics of R programming.Apply R in data manipulation, visualization, and modeling.2. Data Science Session 1Overview:-The first session introduces core concepts of Data Science, including data collection, preprocessing, and exploration.Learning Outcomes:-Understand the foundational concepts of Data Science.Learn how to collect and prepare data for analysis.3. Data Science Session 2Overview:-This session delves deeper into data analysis techniques and introduces the basics of statistical modeling.Learning Outcomes:-Explore advanced data analysis techniques.Begin working with statistical models in Data Science.4. Data Science Process OverviewOverview:-Provides a comprehensive overview of the Data Science process, from data collection to model deployment.Learning Outcomes:-Gain a holistic understanding of the Data Science workflow.Learn about each stage of the Data Science process.5. Data ScientistOverview:-Focuses on the role of a Data Scientist, covering key skills, tools, and methodologies used in the field.Learning Outcomes:-Understand the responsibilities and skillset of a Data Scientist.Get acquainted with essential tools and techniques.6. Data Scientist AIML End to EndOverview:-Explores the end-to-end process of applying Artificial Intelligence and Machine Learning in Data Science projects.Learning Outcomes:-Learn how to integrate AI and ML techniques in Data Science workflows.Complete an end-to-end AIML project.7. Data Science Process OverviewOverview:-Another overview focused on reinforcing the understanding of the Data Science process.Learning Outcomes:-Solidify your understanding of the Data Science lifecycle.Review key concepts and stages in the process.8. Data Science Process Overview End to End AIMLOverview:-This session provides a detailed walkthrough of the entire Data Science process with an emphasis on AIML integration.Learning Outcomes:-Master the end-to-end Data Science process.Apply AIML techniques to real-world Data Science problems.9. Introduction to R for Data ScienceOverview:-Introduces R programming with a focus on its application in Data Science, including data manipulation and visualization.Learning Outcomes:-Get started with R programming for Data Science.Learn to use R for basic data analysis tasks.10. R Programming Basics AIML End to EndOverview:-Covers the basic syntax and structures of R, with a focus on applying them in AIML contexts.Learning Outcomes:-Learn the fundamentals of R programming.Apply R in basic AIML tasks and projects.11. R Programming Part 2Overview:-This section builds on the basics, introducing more advanced R programming techniques, including data wrangling and modeling.Learning Outcomes:Develop advanced R programming skills.Implement complex data wrangling and modeling tasks using R.
